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--- news:news [2022/01/02 13:51] – Wolfgang Viechtbauer
+++ news:news [2022/04/21 15:45] – Wolfgang Viechtbauer
@@ Line 2: / Line 2: @@
 ~~NOTOC~~
+==== 2022-04-21: Version 3.4 Released on CRAN ====
+A new version of the metafor package (version 3.4) has been released. Some highlights:
+  * The ''[[https://wviechtb.github.io/metafor/reference/vcalc.html|vcalc()]]'' function was added. With this function, one can construct or approximate the variance-covariance matrix of dependent effect sizes for a wide variety of circumstances.
+  * The ''[[https://wviechtb.github.io/metafor/reference/robust.html|robust()]]'' function, for cluster-robust inferences (also known as robust variance estimation), now interfaces fully with the excellent [[https://cran.r-project.org/package=clubSandwich|clubSandwich]] package, so one can make use of the improved methods therein.
+  * For meta-analyses involving complex dependency structures, ''vcalc()'', ''rma.mv()'', and ''robust(..., clubSandwich=TRUE)'' are all part of a general workflow that can handle the vast majority of dependencies in meta-analyses, as described [[https://wviechtb.github.io/metafor/reference/misc-recs.html#general-workflow-for-meta-analyses-involving-complex-dependency-structures|here]].
+  * The ''[[https://wviechtb.github.io/metafor/reference/aggregate.escalc.html|aggregate.escalc()]]'' method -- for aggregating ''escalc'' datasets with dependent effect sizes to a higher level -- has a new 'structure' (''struct="CS+CAR"'') if there are effects at multiple time points and multiple effect sizes at these time points.
+  * A few more measures were added to ''[[https://wviechtb.github.io/metafor/reference/escalc.html|escalc()]]'': ''"MPORM"'' for computing marginal log odds ratios based on marginal 2x2 tables and ''"REH"'' for computing the (log transformed) relative excess heterozygosity (this is a bit more esoteric stuff).
+  * ''[[https://wviechtb.github.io/metafor/reference/rma.glmm.html|rma.glmm()]]'' -- for meta-analytic generalized linear (mixed-effects) models -- allows more flexibility in the coding of the group variable and whether the random study effects should be allowed to be correlated with the random group effects.
+  * Even more optimizer choices for ''[[https://wviechtb.github.io/metafor/reference/rma.mv.html#note|rma.mv()]]'' (including a subspace-searching simplex algorithm and the Barzilai-Borwein gradient decent method): If you can't get the model to converge with any of the available options, all hope is lost!
+  * All datasets that used to be part of the metafor package have now been moved to the [[https://wviechtb.github.io/metadat/|metadat]] package (which now includes even more meta-analysis datasets).
+  * A bunch of smaller convenience features (e.g., some ''as.data.frame()'' methods, a ''refit'' argument in ''anova()'', more use of a ''data'' argument), a few clever tricks with a custom package environment to store settings, and free candy (not really).
+  * Lots of documentation updates, including a description of [[https://wviechtb.github.io/metafor/reference/misc-models.html|fixed- versus random-effects models]], [[https://wviechtb.github.io/metafor/reference/misc-recs.html|some recommended practices]], and [[https://wviechtb.github.io/metafor/reference/misc-options.html|miscellaneous options and features]].
+The full changelog can be found [[:updates#version_34-0_2022-04-21|here]].
+==== 2022-03-20: Forest Plot with Exact Confidence Intervals ====
+A question was recently raised on the [[https://stat.ethz.ch/mailman/listinfo/r-sig-meta-analysis|R-sig-meta-analysis]] mailing list that asked about the difference between the confidence intervals shown in forest plots and those computed based on 'exact' methods (see [[https://stat.ethz.ch/pipermail/r-sig-meta-analysis/2022-March/003947.html|here]] for the question and [[https://stat.ethz.ch/pipermail/r-sig-meta-analysis/2022-March/003950.html|here]] for my response). Using a slightly more common example of a meta-analysis based on $2 \times 2$ table data, I have written up a little illustration to show how one can create a [[tips:forest_plot_with_exact_cis|forest plot with exact confidence intervals]].
+==== 2022-03-12: Over 10,000 Citations ====
+Since I don't obsessively check my Google Scholar profile like everybody else does, it is by mere coincidence that I noticed that my [[https://doi.org/10.18637/jss.v036.i03|JSS paper about the metafor package]] has now been [[https://scholar.google.com/scholar?oi=bibs&hl=en&cites=8753688964455559681|cited more than 10,000 times]] (of course, like everybody else, I will ignore the Web of Science count, which isn't quite there yet ...). I greatly appreciate that people are citing the paper and hence supporting the creation and maintenance of this R package in this way. It can still be difficult to receive proper credit for software development in academia, so citing the software is one of the best ways that you can support developers in their work (aside from donating a million bucks you happen to have lying around). I think it also helps if there is a paper or book about the software, which is sometimes a bit easier to cite than the software itself (what was again the APA style for citing software?) and citation counts are more easily tracked for papers/books than citations of the software itself.
+==== 2022-03-06: Specifying Inputs to the rma() Function ====
+Unfortunately, I have seen a number of cases where users of the metafor package have misspecified the inputs to the ''rma()'' function, giving the standard errors of the effect sizes as an unnamed second argument. This will lead to incorrect results. To explain the problem in more detail (and so that I can simply point people to a place where this issue is explained thoroughly), I have written up [[tips:input_to_rma_function|this discussion]].
 ==== 2022-01-02: More Forest Plot Examples ====